Supervision 3 (12-13 March, 2020)¶
Instructions¶
- Read through the instruction carefully. You may face problems if you overlook any of the steps.
- The instruction for data collection via APIs is written in Google Colab, a free jupyter environment that requires no setup to use and runs python entirely in the cloud. You need log in with your Google Account to use this free platform. If you do not have Google Account previously, please apply a new account via this link before the supervision. Know more about Google Colab, please check this link.
- If you do not have Twitter account, please apply one via this Twitter Signup
Note: functions and filename are highlighted in this document.
Supervision overview¶
In this exercise, you will familiarise yourself with collect data via Application programming interface(APIs), spatial visualization with collected data and creating a formal map on QGIS.
1. Collect Tweets via API¶
Apply API from Twitter (5 mins)¶
The Twitter API (application programming interface) is your gateway to accessing Twitter data. A guidance on setting up Twitter APi: http://social-metrics.org/api-keys/
- Open your broswer and open Twitter APIs apply.
- Click Apply for a developer account. I strongly recommend you to go through restricted uses of Twitter APIs before applying APIs from twitter.
- Log in Twitter with your Twitter account.
- Navigate to
your accounton the menu and clickApps. Then, clickcreate an appbutton. - In the application page, input information for required items and click
createbutton. - Get your authentication details
Note: please find this page if you fails to apply account during the supervision. notice that these APIs will be expeired after supervision.
Collect data using API and save into tweet.csv file (10mins)¶
In this supervision, we will use python package to help collect data from Twitter via API. If you are new to Python, you may wish to know basic python. Please see the document for python 3.x: https://docs.python.org/3/
The package used in this exercise is Tweepy, a more detailed tutorial: http://docs.tweepy.org/en/latest/.
- Connect to the twitter RESTful API to access twitter data with Python.
- Generate custom queries that download tweet data into Python using Tweepy.
Step 0: install tweepy and other essential package¶
Only install the package at the first time. code for installation:
pip install tweepy
Step 1: import all necessary packages/ libraries¶
In [1]:
import tweepy
import csv
import time
import os.path
Step 2: define your key¶
In [2]:
#Variables that contains the user credentials to access Twitter API
access_token = "528087151-Jh0dy5uVn8O4OAI2E8zh6UtdDv7HemxvnpbrZwmo"
access_token_secret = "94Jbu2xeGEtnXWivKgPgYoKAfzL1zxZAlmHxeWOPpwL8i"
consumer_key = "coXtHK8eQraTJ6vDTZXus1DrW"
consumer_secret = "5mkF2ac563pAJ5Xaw8CuOCtTEmK8OeV0SCgRqjPvZQWtnKseFq"
auth = tweepy.auth.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
In [3]:
# test authentication
try:
api.verify_credentials()
print("Authentication OK")
except:
print("Error during authentication")
Authentication OK
Step 3: search Twitter for Tweets and save data to csv¶
- what dataset you need? e.g. What is the public sentiment towards coronavirus in London?
- set keywords e.g. coronavirus, corona virus, virus, covid19, nCoV2019
- set geocode e.g. Cambridge: 52.2,0.12,2mi, London: 51.53,-0.83,50mi
- collect tweets from the previous 15 days
https://developer.twitter.com/en/docs/tutorials/filtering-tweets-by-location
The government provide flood information service via this link. Please open the link and check which cities has been issued a flood warining.
In [6]:
file_exists = os.path.isfile('tweet.csv')
keywords = "flood" or "storm*"
# or "shower*" or "storm*" or "rain"
# keywords = "i"
#Cambridge: 52.2,0.12,2mi
#London: 51.53,-0.83,50mi
search_results = api.search(q=keywords, lang="en", count=80, geocode = "53.96,-1.08,10mi")
print(search_results)
with open('tweet.csv',mode = 'w',encoding='utf-8') as f:
headers = ['id', 'tweet', 'coordinates']
writer = csv.DictWriter(f, delimiter=',', lineterminator='\n',fieldnames=headers)
writer.writeheader()
for status in search_results:
print(status)
if status.coordinates == None:
writer.writerow({'id': status.id, 'tweet': status.text})
else:
writer.writerow({'id': status.id, 'tweet': status.text, 'coordinates': status.coordinates['coordinates']})
[Status(_api=<tweepy.api.API object at 0x10cbcac10>, _json={'created_at': 'Fri Mar 06 20:14:09 +0000 2020', 'id': 1236022277218566144, 'id_str': '1236022277218566144', 'text': 'Mud from the last York flood. Brilliant for gardens and allotments, as I said to a baby pushing York man, who proba… https://t.co/Yv1LE1RmTY', 'truncated': True, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [{'url': 'https://t.co/Yv1LE1RmTY', 'expanded_url': 'https://twitter.com/i/web/status/1236022277218566144', 'display_url': 'twitter.com/i/web/status/1…', 'indices': [117, 140]}]}, 'metadata': {'iso_language_code': 'en', 'result_type': 'recent'}, 'source': '<a href="http://instagram.com" rel="nofollow">Instagram</a>', 'in_reply_to_status_id': None, 'in_reply_to_status_id_str': None, 'in_reply_to_user_id': None, 'in_reply_to_user_id_str': None, 'in_reply_to_screen_name': None, 'user': {'id': 176878577, 'id_str': '176878577', 'name': 'Glyn Watkins', 'screen_name': 'bradwan1', 'location': 'Bradford, West Yorkshire', 'description': 'Poet, writer showman. Fan of pies, pylons, beer & much else. 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Brilliant for gardens and allotments, as I said to a baby pushing York man, who proba… https://t.co/Yv1LE1RmTY', truncated=True, entities={'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [{'url': 'https://t.co/Yv1LE1RmTY', 'expanded_url': 'https://twitter.com/i/web/status/1236022277218566144', 'display_url': 'twitter.com/i/web/status/1…', 'indices': [117, 140]}]}, metadata={'iso_language_code': 'en', 'result_type': 'recent'}, source='Instagram', source_url='http://instagram.com', in_reply_to_status_id=None, in_reply_to_status_id_str=None, in_reply_to_user_id=None, in_reply_to_user_id_str=None, in_reply_to_screen_name=None, author=User(_api=<tweepy.api.API object at 0x10cbcac10>, _json={'id': 176878577, 'id_str': '176878577', 'name': 'Glyn Watkins', 'screen_name': 'bradwan1', 'location': 'Bradford, West Yorkshire', 'description': 'Poet, writer showman. Fan of pies, pylons, beer & much else. 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2. Sentiment analysis with content of tweets¶
- the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral
- an example of AFINN dictionary https://github.com/fnielsen/afinn
- analyse the sentiment of tweets to show Londoners' attitude towards coronavirus
Step 0 & 1: install and import necessary packages¶
In [7]:
from __future__ import division
from afinn import Afinn
import numpy as np
import pandas as pd
# importing the necessary dependencies
from collections import Counter
from matplotlib import pyplot as plt
from bs4 import BeautifulSoup
from nltk.corpus import wordnet, stopwords
from spacy.lang.en.stop_words import STOP_WORDS
from nltk.tokenize import WordPunctTokenizer
import re
#import seaborn as sns
#import pandas as pd
import nltk
import warnings
#nltk.download('all')
warnings.filterwarnings("ignore")
%matplotlib inline
An example of sentiment analysis - AFINN
In [8]:
afinn = Afinn(emoticons = True)
afinn.score('This is utterly excellent!;)')
Out[8]:
5.0
Step 2: remove duplicate tweets¶
In [9]:
df = pd.read_csv('tweet.csv')
df.drop_duplicates(subset ="tweet", keep = False, inplace = True)
df
Out[9]:
| id | tweet | coordinates | |
|---|---|---|---|
| 0 | 1236022277218566144 | Mud from the last York flood. Brilliant for ga... | [-1.0803, 53.9583] |
| 1 | 1235972950555230208 | Flood Community Drop in session Monday 5-7pm 1... | NaN |
| 2 | 1235962305545977856 | RT @FishergateYork: Flood Community Drop in se... | NaN |
| 3 | 1235946017067057152 | First up was Stamford Bridge. Some #flooddefen... | NaN |
| 4 | 1235935231007485954 | Flood Community Drop in session Wedneday 11th ... | NaN |
| 5 | 1235926940495867905 | @HouseofCommons @JulianSturdy Hi @JulianSturdy... | NaN |
| 6 | 1235924364111081472 | With an unsettled outlook for the weekend our ... | NaN |
| 7 | 1235921488101937153 | RT @vikloumcc: Come on #York. I’ve met so many... | NaN |
| 8 | 1235908081109827590 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] |
| 9 | 1235885403917385728 | @HighFarndale Not good in terms of environment... | NaN |
| 10 | 1235879276622331904 | @4TheArsenal_ Personally flood he hid from thi... | NaN |
| 11 | 1235861179450290177 | NEWS - Grants of up to £5,000 are being offere... | NaN |
| 12 | 1235845935650836482 | 8:30am - Temple Hirst Junction \nHaven't been ... | NaN |
| 13 | 1235839866555916288 | RT @Mindlockyork: Check out the boss lady chat... | NaN |
| 18 | 1235684109269131264 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] |
| 20 | 1235681865262075906 | There is still plenty of cleaning up to do, st... | NaN |
| 21 | 1235681852867870721 | @NorthYorksFire responded to lots of Flood inc... | NaN |
| 22 | 1235681828197085185 | On 9th Feb the @EnvAgency issued a Flood Alert... | NaN |
| 24 | 1235674944861802496 | Come on #York. I’ve met so many who have exper... | NaN |
| 27 | 1235649502498840576 | Bingo Update.. (see below) 🥺\n\nNow it’s time ... | NaN |
| 30 | 1235642535608864768 | RT @YorkGreens: Cllr @DeniseCraghill explains ... | NaN |
| 32 | 1235637442838966273 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN |
| 33 | 1235635793793867777 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN |
| 34 | 1235635720578007041 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN |
| 35 | 1235598769543991302 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] |
| 36 | 1235596055607160834 | Back to normal: River Ouse at Naburn Lock 15:4... | [-1.096129, 53.893899] |
| 37 | 1235596032861507586 | Back to normal: Upper River Ouse 15:45 5 March... | [-1.169873, 53.996804] |
| 38 | 1235586941569163264 | @JulianSturdy @ThomlinsonAlfie @yorkpress @Cit... | NaN |
| 39 | 1235584978978775044 | Julie Foley on the Flood & Coastal Erosion... | NaN |
| 40 | 1235583883220934659 | RT @GSwinburn: Hi @NigelCllr can you confirm i... | NaN |
| 41 | 1235578669684252678 | RT @YorkshirePFR: @CityofYork Council Flood Ri... | NaN |
| 42 | 1235578487798300677 | Thanks @FloodframeUk for sharing information a... | NaN |
| 43 | 1235577853632266242 | @CityofYork Council Flood Risk Manager and @Yo... | NaN |
| 44 | 1235567773956349952 | Boris Johnson defends decision not to visit UK... | NaN |
| 45 | 1235567346342715394 | Feelings flood your interactions with others t... | NaN |
| 46 | 1235565759637721095 | RT @vikloumcc: Today I’m at the @NatFloodForum... | NaN |
| 48 | 1235533364846833666 | Route 7 flood update - East / West Cowick is n... | NaN |
| 49 | 1235526957179133953 | NEWS - 10,000 litres of water is being pumped ... | NaN |
| 51 | 1235518102105919495 | Hi @NigelCllr can you confirm if @cityofyork w... | NaN |
| 52 | 1235512928704647168 | Some key messages from the Minister of Floodin... | NaN |
| 53 | 1235505901613826048 | Today I’m at the @NatFloodForum conference to ... | NaN |
| 57 | 1235495211494764547 | It's great to see New Walk being hosed down so... | NaN |
| 58 | 1235493008512073728 | @DWildheart Okay: '...Flood' I was already fam... | NaN |
| 59 | 1235482864030027778 | Flood Warning: River Ouse at Naburn Lock 08:01... | [-1.096129, 53.893899] |
| 60 | 1235482841267556353 | Flood Alert: Upper River Ouse 08:11 5 March ht... | [-1.169873, 53.996804] |
| 61 | 1235482818555215873 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] |
| 62 | 1235358937937063939 | RT @RobStoneman: Protect cities from flooding ... | NaN |
| 66 | 1235316708715397120 | Flood Warning: River Ouse at Naburn Lock 21:01... | [-1.096129, 53.893899] |
| 67 | 1235316685831315464 | Flood Alert: Upper River Ouse 21:04 4 March ht... | [-1.169873, 53.996804] |
| 68 | 1235311644194074626 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] |
| 73 | 1235291158923956227 | @PatersonLou The flood barriers aren't working... | NaN |
| 75 | 1235252399121539073 | RT @RiverOuseYork: River Ouse York water level... | NaN |
| 77 | 1235248670355972102 | River Ouse York water level: 2.12m at 16:00. C... | NaN |
| 78 | 1235237319558475777 | RT @RiverOuseYork: River Ouse York water level... | NaN |
| 79 | 1235233686649528320 | Flood Alert: Upper River Ouse 15:35 4 March ht... | [-1.169873, 53.996804] |
Step 3: clean data¶
- remove unuseful characters and hyper links
In [10]:
#removing all the unuseful characters such as "@,#" and hyper links "https...."
tok = WordPunctTokenizer()
pat1 = r'@[A-Za-z0-9]+' # Removing @text situations
pat2 = r'https?://[A-Za-z0-9./]+' # Removing hyperlinks
combined_pat = r'|'.join((pat1, pat2))
def tweet_cleaner(text):
soup = BeautifulSoup(text, 'lxml') # converting possibly unconverted html to text
souped = soup.get_text()
stripped = re.sub(combined_pat, '', souped)
letters_only = re.sub("[^a-zA-Z]", " ", stripped) # Getting read of non text characters
lower_case = letters_only.lower()
# During the letters_only process two lines above, it has created unnecessay white spaces,
# We tokenize and join together to remove unneccessary white spaces
words = tok.tokenize(lower_case)
return (" ".join(words)).strip()
# Looping over every tweet in the data set and applying the tweet_cleaner function it.
clean_tweet_texts = []
for tweet in df.tweet:
clean_tweet_texts.append(tweet_cleaner(tweet))
clean_tweet_texts
#creating a new DataFrame for the filtered text
df['clean_tweet'] = clean_tweet_texts
df
Out[10]:
| id | tweet | coordinates | clean_tweet | |
|---|---|---|---|---|
| 0 | 1236022277218566144 | Mud from the last York flood. Brilliant for ga... | [-1.0803, 53.9583] | mud from the last york flood brilliant for gar... |
| 1 | 1235972950555230208 | Flood Community Drop in session Monday 5-7pm 1... | NaN | flood community drop in session monday pm th m... |
| 2 | 1235962305545977856 | RT @FishergateYork: Flood Community Drop in se... | NaN | rt flood community drop in session wedneday th... |
| 3 | 1235946017067057152 | First up was Stamford Bridge. Some #flooddefen... | NaN | first up was stamford bridge some flooddefence... |
| 4 | 1235935231007485954 | Flood Community Drop in session Wedneday 11th ... | NaN | flood community drop in session wedneday th ma... |
| 5 | 1235926940495867905 | @HouseofCommons @JulianSturdy Hi @JulianSturdy... | NaN | hi looking forward to hearing why you voted ag... |
| 6 | 1235924364111081472 | With an unsettled outlook for the weekend our ... | NaN | with an unsettled outlook for the weekend our ... |
| 7 | 1235921488101937153 | RT @vikloumcc: Come on #York. I’ve met so many... | NaN | rt come on york i ve met so many who have expe... |
| 8 | 1235908081109827590 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... |
| 9 | 1235885403917385728 | @HighFarndale Not good in terms of environment... | NaN | not good in terms of environment wildlife clim... |
| 10 | 1235879276622331904 | @4TheArsenal_ Personally flood he hid from thi... | NaN | personally flood he hid from this he s bumblin... |
| 11 | 1235861179450290177 | NEWS - Grants of up to £5,000 are being offere... | NaN | news grants of up to are being offered to floo... |
| 12 | 1235845935650836482 | 8:30am - Temple Hirst Junction \nHaven't been ... | NaN | am temple hirst junction haven t been able to ... |
| 13 | 1235839866555916288 | RT @Mindlockyork: Check out the boss lady chat... | NaN | rt check out the boss lady chatting to from mi... |
| 18 | 1235684109269131264 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... |
| 20 | 1235681865262075906 | There is still plenty of cleaning up to do, st... | NaN | there is still plenty of cleaning up to do sta... |
| 21 | 1235681852867870721 | @NorthYorksFire responded to lots of Flood inc... | NaN | responded to lots of flood incidents including... |
| 22 | 1235681828197085185 | On 9th Feb the @EnvAgency issued a Flood Alert... | NaN | on th feb the issued a flood alert for the upp... |
| 24 | 1235674944861802496 | Come on #York. I’ve met so many who have exper... | NaN | come on york i ve met so many who have experie... |
| 27 | 1235649502498840576 | Bingo Update.. (see below) 🥺\n\nNow it’s time ... | NaN | bingo update see below now it s time to raise ... |
| 30 | 1235642535608864768 | RT @YorkGreens: Cllr @DeniseCraghill explains ... | NaN | rt cllr explains what are doing in york to tac... |
| 32 | 1235637442838966273 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN | updates |
| 33 | 1235635793793867777 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN | updates |
| 34 | 1235635720578007041 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN | updates |
| 35 | 1235598769543991302 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... |
| 36 | 1235596055607160834 | Back to normal: River Ouse at Naburn Lock 15:4... | [-1.096129, 53.893899] | back to normal river ouse at naburn lock march |
| 37 | 1235596032861507586 | Back to normal: Upper River Ouse 15:45 5 March... | [-1.169873, 53.996804] | back to normal upper river ouse march |
| 38 | 1235586941569163264 | @JulianSturdy @ThomlinsonAlfie @yorkpress @Cit... | NaN | from the majority of mps vo |
| 39 | 1235584978978775044 | Julie Foley on the Flood & Coastal Erosion... | NaN | julie foley on the flood coastal erosion risk ... |
| 40 | 1235583883220934659 | RT @GSwinburn: Hi @NigelCllr can you confirm i... | NaN | rt hi can you confirm if was able to access th... |
| 41 | 1235578669684252678 | RT @YorkshirePFR: @CityofYork Council Flood Ri... | NaN | rt council flood risk manager and pm steve wra... |
| 42 | 1235578487798300677 | Thanks @FloodframeUk for sharing information a... | NaN | thanks for sharing information about our surve... |
| 43 | 1235577853632266242 | @CityofYork Council Flood Risk Manager and @Yo... | NaN | council flood risk manager and pm steve wragg ... |
| 44 | 1235567773956349952 | Boris Johnson defends decision not to visit UK... | NaN | boris johnson defends decision not to visit uk... |
| 45 | 1235567346342715394 | Feelings flood your interactions with others t... | NaN | feelings flood your interactions with others t... |
| 46 | 1235565759637721095 | RT @vikloumcc: Today I’m at the @NatFloodForum... | NaN | rt today i m at the conference to learn and sh... |
| 48 | 1235533364846833666 | Route 7 flood update - East / West Cowick is n... | NaN | route flood update east west cowick is now ope... |
| 49 | 1235526957179133953 | NEWS - 10,000 litres of water is being pumped ... | NaN | news litres of water is being pumped out of lo... |
| 51 | 1235518102105919495 | Hi @NigelCllr can you confirm if @cityofyork w... | NaN | hi can you confirm if was able to access the b... |
| 52 | 1235512928704647168 | Some key messages from the Minister of Floodin... | NaN | some key messages from the minister of floodin... |
| 53 | 1235505901613826048 | Today I’m at the @NatFloodForum conference to ... | NaN | today i m at the conference to learn and share... |
| 57 | 1235495211494764547 | It's great to see New Walk being hosed down so... | NaN | it s great to see new walk being hosed down so... |
| 58 | 1235493008512073728 | @DWildheart Okay: '...Flood' I was already fam... | NaN | okay flood i was already familiar with and i r... |
| 59 | 1235482864030027778 | Flood Warning: River Ouse at Naburn Lock 08:01... | [-1.096129, 53.893899] | flood warning river ouse at naburn lock march |
| 60 | 1235482841267556353 | Flood Alert: Upper River Ouse 08:11 5 March ht... | [-1.169873, 53.996804] | flood alert upper river ouse march |
| 61 | 1235482818555215873 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... |
| 62 | 1235358937937063939 | RT @RobStoneman: Protect cities from flooding ... | NaN | rt protect cities from flooding by making them... |
| 66 | 1235316708715397120 | Flood Warning: River Ouse at Naburn Lock 21:01... | [-1.096129, 53.893899] | flood warning river ouse at naburn lock march |
| 67 | 1235316685831315464 | Flood Alert: Upper River Ouse 21:04 4 March ht... | [-1.169873, 53.996804] | flood alert upper river ouse march |
| 68 | 1235311644194074626 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... |
| 73 | 1235291158923956227 | @PatersonLou The flood barriers aren't working... | NaN | the flood barriers aren t working so the govt ... |
| 75 | 1235252399121539073 | RT @RiverOuseYork: River Ouse York water level... | NaN | rt river ouse york water level m at chance of ... |
| 77 | 1235248670355972102 | River Ouse York water level: 2.12m at 16:00. C... | NaN | river ouse york water level m at chance of flo... |
| 78 | 1235237319558475777 | RT @RiverOuseYork: River Ouse York water level... | NaN | rt river ouse york water level m at chance of ... |
| 79 | 1235233686649528320 | Flood Alert: Upper River Ouse 15:35 4 March ht... | [-1.169873, 53.996804] | flood alert upper river ouse march |
step 3: score tweets and save to a new csv¶
In [12]:
# Sentiment analysis with AFINN
afinn = Afinn(emoticons=True)
afinn_scores = [afinn.score(clean_tweet) for clean_tweet in df.clean_tweet]
df['sentiment'] = afinn_scores
#save to a new csv
#df.to_csv('tweet_scores.csv')
df
Out[12]:
| id | tweet | coordinates | clean_tweet | sentiment | |
|---|---|---|---|---|---|
| 0 | 1236022277218566144 | Mud from the last York flood. Brilliant for ga... | [-1.0803, 53.9583] | mud from the last york flood brilliant for gar... | 4.0 |
| 1 | 1235972950555230208 | Flood Community Drop in session Monday 5-7pm 1... | NaN | flood community drop in session monday pm th m... | -1.0 |
| 2 | 1235962305545977856 | RT @FishergateYork: Flood Community Drop in se... | NaN | rt flood community drop in session wedneday th... | -1.0 |
| 3 | 1235946017067057152 | First up was Stamford Bridge. Some #flooddefen... | NaN | first up was stamford bridge some flooddefence... | 1.0 |
| 4 | 1235935231007485954 | Flood Community Drop in session Wedneday 11th ... | NaN | flood community drop in session wedneday th ma... | -1.0 |
| 5 | 1235926940495867905 | @HouseofCommons @JulianSturdy Hi @JulianSturdy... | NaN | hi looking forward to hearing why you voted ag... | 2.0 |
| 6 | 1235924364111081472 | With an unsettled outlook for the weekend our ... | NaN | with an unsettled outlook for the weekend our ... | -1.0 |
| 7 | 1235921488101937153 | RT @vikloumcc: Come on #York. I’ve met so many... | NaN | rt come on york i ve met so many who have expe... | 6.0 |
| 8 | 1235908081109827590 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... | -1.0 |
| 9 | 1235885403917385728 | @HighFarndale Not good in terms of environment... | NaN | not good in terms of environment wildlife clim... | -2.0 |
| 10 | 1235879276622331904 | @4TheArsenal_ Personally flood he hid from thi... | NaN | personally flood he hid from this he s bumblin... | -1.0 |
| 11 | 1235861179450290177 | NEWS - Grants of up to £5,000 are being offere... | NaN | news grants of up to are being offered to floo... | -2.0 |
| 12 | 1235845935650836482 | 8:30am - Temple Hirst Junction \nHaven't been ... | NaN | am temple hirst junction haven t been able to ... | -3.0 |
| 13 | 1235839866555916288 | RT @Mindlockyork: Check out the boss lady chat... | NaN | rt check out the boss lady chatting to from mi... | 0.0 |
| 18 | 1235684109269131264 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... | -1.0 |
| 20 | 1235681865262075906 | There is still plenty of cleaning up to do, st... | NaN | there is still plenty of cleaning up to do sta... | 0.0 |
| 21 | 1235681852867870721 | @NorthYorksFire responded to lots of Flood inc... | NaN | responded to lots of flood incidents including... | 0.0 |
| 22 | 1235681828197085185 | On 9th Feb the @EnvAgency issued a Flood Alert... | NaN | on th feb the issued a flood alert for the upp... | -1.0 |
| 24 | 1235674944861802496 | Come on #York. I’ve met so many who have exper... | NaN | come on york i ve met so many who have experie... | 6.0 |
| 27 | 1235649502498840576 | Bingo Update.. (see below) 🥺\n\nNow it’s time ... | NaN | bingo update see below now it s time to raise ... | 1.0 |
| 30 | 1235642535608864768 | RT @YorkGreens: Cllr @DeniseCraghill explains ... | NaN | rt cllr explains what are doing in york to tac... | 0.0 |
| 32 | 1235637442838966273 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN | updates | 0.0 |
| 33 | 1235635793793867777 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN | updates | 0.0 |
| 34 | 1235635720578007041 | @floodmary @InnesThomson @pinktrackies @ADA_up... | NaN | updates | 0.0 |
| 35 | 1235598769543991302 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... | -1.0 |
| 36 | 1235596055607160834 | Back to normal: River Ouse at Naburn Lock 15:4... | [-1.096129, 53.893899] | back to normal river ouse at naburn lock march | 0.0 |
| 37 | 1235596032861507586 | Back to normal: Upper River Ouse 15:45 5 March... | [-1.169873, 53.996804] | back to normal upper river ouse march | 0.0 |
| 38 | 1235586941569163264 | @JulianSturdy @ThomlinsonAlfie @yorkpress @Cit... | NaN | from the majority of mps vo | 0.0 |
| 39 | 1235584978978775044 | Julie Foley on the Flood & Coastal Erosion... | NaN | julie foley on the flood coastal erosion risk ... | -2.0 |
| 40 | 1235583883220934659 | RT @GSwinburn: Hi @NigelCllr can you confirm i... | NaN | rt hi can you confirm if was able to access th... | -1.0 |
| 41 | 1235578669684252678 | RT @YorkshirePFR: @CityofYork Council Flood Ri... | NaN | rt council flood risk manager and pm steve wra... | -2.0 |
| 42 | 1235578487798300677 | Thanks @FloodframeUk for sharing information a... | NaN | thanks for sharing information about our surve... | 2.0 |
| 43 | 1235577853632266242 | @CityofYork Council Flood Risk Manager and @Yo... | NaN | council flood risk manager and pm steve wragg ... | -2.0 |
| 44 | 1235567773956349952 | Boris Johnson defends decision not to visit UK... | NaN | boris johnson defends decision not to visit uk... | -4.0 |
| 45 | 1235567346342715394 | Feelings flood your interactions with others t... | NaN | feelings flood your interactions with others t... | 3.0 |
| 46 | 1235565759637721095 | RT @vikloumcc: Today I’m at the @NatFloodForum... | NaN | rt today i m at the conference to learn and sh... | -1.0 |
| 48 | 1235533364846833666 | Route 7 flood update - East / West Cowick is n... | NaN | route flood update east west cowick is now ope... | 0.0 |
| 49 | 1235526957179133953 | NEWS - 10,000 litres of water is being pumped ... | NaN | news litres of water is being pumped out of lo... | 0.0 |
| 51 | 1235518102105919495 | Hi @NigelCllr can you confirm if @cityofyork w... | NaN | hi can you confirm if was able to access the b... | -1.0 |
| 52 | 1235512928704647168 | Some key messages from the Minister of Floodin... | NaN | some key messages from the minister of floodin... | 0.0 |
| 53 | 1235505901613826048 | Today I’m at the @NatFloodForum conference to ... | NaN | today i m at the conference to learn and share... | -1.0 |
| 57 | 1235495211494764547 | It's great to see New Walk being hosed down so... | NaN | it s great to see new walk being hosed down so... | 3.0 |
| 58 | 1235493008512073728 | @DWildheart Okay: '...Flood' I was already fam... | NaN | okay flood i was already familiar with and i r... | 1.0 |
| 59 | 1235482864030027778 | Flood Warning: River Ouse at Naburn Lock 08:01... | [-1.096129, 53.893899] | flood warning river ouse at naburn lock march | -3.0 |
| 60 | 1235482841267556353 | Flood Alert: Upper River Ouse 08:11 5 March ht... | [-1.169873, 53.996804] | flood alert upper river ouse march | -1.0 |
| 61 | 1235482818555215873 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... | -1.0 |
| 62 | 1235358937937063939 | RT @RobStoneman: Protect cities from flooding ... | NaN | rt protect cities from flooding by making them... | 5.0 |
| 66 | 1235316708715397120 | Flood Warning: River Ouse at Naburn Lock 21:01... | [-1.096129, 53.893899] | flood warning river ouse at naburn lock march | -3.0 |
| 67 | 1235316685831315464 | Flood Alert: Upper River Ouse 21:04 4 March ht... | [-1.169873, 53.996804] | flood alert upper river ouse march | -1.0 |
| 68 | 1235311644194074626 | Flood Alert: Tidal River Ouse from Naburn Lock... | [-1.11412, 53.822911] | flood alert tidal river ouse from naburn lock ... | -1.0 |
| 73 | 1235291158923956227 | @PatersonLou The flood barriers aren't working... | NaN | the flood barriers aren t working so the govt ... | -1.0 |
| 75 | 1235252399121539073 | RT @RiverOuseYork: River Ouse York water level... | NaN | rt river ouse york water level m at chance of ... | 1.0 |
| 77 | 1235248670355972102 | River Ouse York water level: 2.12m at 16:00. C... | NaN | river ouse york water level m at chance of flo... | 1.0 |
| 78 | 1235237319558475777 | RT @RiverOuseYork: River Ouse York water level... | NaN | rt river ouse york water level m at chance of ... | 1.0 |
| 79 | 1235233686649528320 | Flood Alert: Upper River Ouse 15:35 4 March ht... | [-1.169873, 53.996804] | flood alert upper river ouse march | -1.0 |
Step 4: basic statistic analysis of sentiment result¶
In [13]:
df['sentiment'].describe()
Out[13]:
count 55.000000 mean -0.090909 std 2.048240 min -4.000000 25% -1.000000 50% -1.000000 75% 0.500000 max 6.000000 Name: sentiment, dtype: float64
3. Discourse analysis¶
- topics of interest
- wordclouds
English keyword extractions¶
For the detail of further cleaning tweets, please see https://github.com/sillsphelyx/Twitter_text_cleaning
Step 1: filter English tweets (remove)¶
In [15]:
#creating a new DataFrame for the filtered text
clean_df = pd.DataFrame(df['id'],columns=['id'])
clean_df['text'] = clean_tweet_texts
# Removing non-english text
# This function takes in as input a sentence and returns only english words
def filter_for_english(sent):
return (" ".join(w for w in nltk.wordpunct_tokenize(sent) \
if len(wordnet.synsets(w)) > 3 ))
#looping over all tweets to return contained english words
checked_for_english = [filter_for_english(sent) for sent in clean_df.text]
# adding a column with tweets that are in English
clean_df['English_text'] = checked_for_english
# adding count columns for number of words per tweet for both clean_text and English only text
clean_df['Clean_text_len'] = [len(t.split()) for t in clean_df.text]
clean_df['English_word_count'] = [len(t.split()) for t in clean_df.English_text] # Useful for filtering dataframe on english text
#Filter dataframe by choosing rows that have atleast one english word
english_df = clean_df[clean_df['English_word_count'] > 0][['id', 'English_text']]
english_df.head()
clean_df
Out[15]:
| id | text | English_text | Clean_text_len | English_word_count | |
|---|---|---|---|---|---|
| 0 | 1236022277218566144 | mud from the last york flood brilliant for gar... | mud last flood brilliant gardens as i said a b... | 22 | 12 |
| 1 | 1235972950555230208 | flood community drop in session monday pm th m... | flood community drop in session pm march meeti... | 13 | 9 |
| 2 | 1235962305545977856 | rt flood community drop in session wedneday th... | flood community drop in session march pm church | 16 | 8 |
| 3 | 1235946017067057152 | first up was stamford bridge some flooddefence... | first up was bridge some still in place can cl... | 20 | 13 |
| 4 | 1235935231007485954 | flood community drop in session wedneday th ma... | flood community drop in session march pm church | 15 | 8 |
| 5 | 1235926940495867905 | hi looking forward to hearing why you voted ag... | looking forward hearing voted a motion | 13 | 6 |
| 6 | 1235924364111081472 | with an unsettled outlook for the weekend our ... | unsettled area incident rooms in will be | 18 | 7 |
| 7 | 1235921488101937153 | rt come on york i ve met so many who have expe... | come on i met so have experienced flooding is ... | 24 | 13 |
| 8 | 1235908081109827590 | flood alert tidal river ouse from naburn lock ... | flood alert lock march | 11 | 4 |
| 9 | 1235885403917385728 | not good in terms of environment wildlife clim... | good in terms change flood | 12 | 5 |
| 10 | 1235879276622331904 | personally flood he hid from this he s bumblin... | personally flood hid s bumbling way through | 12 | 7 |
| 11 | 1235861179450290177 | news grants of up to are being offered to floo... | news grants up are being offered flood in | 17 | 8 |
| 12 | 1235845935650836482 | am temple hirst junction haven t been able to ... | am temple junction t been able see flood plain... | 23 | 12 |
| 13 | 1235839866555916288 | rt check out the boss lady chatting to from mi... | check out boss about flood scares a number b | 21 | 9 |
| 18 | 1235684109269131264 | flood alert tidal river ouse from naburn lock ... | flood alert lock march | 11 | 4 |
| 20 | 1235681865262075906 | there is still plenty of cleaning up to do sta... | there is still cleaning up do standing water o... | 23 | 14 |
| 21 | 1235681852867870721 | responded to lots of flood incidents including... | lots flood including flooded properties found | 14 | 6 |
| 22 | 1235681828197085185 | on th feb the issued a flood alert for the upp... | on issued a flood alert upper as a result due ... | 22 | 11 |
| 24 | 1235674944861802496 | come on york i ve met so many who have experie... | come on i met so have experienced flooding is ... | 22 | 13 |
| 27 | 1235649502498840576 | bingo update see below now it s time to raise ... | update see below now s time raise as much as p... | 20 | 14 |
| 30 | 1235642535608864768 | rt cllr explains what are doing in york to tac... | are doing in tackle flooding need invest more ... | 18 | 10 |
| 32 | 1235637442838966273 | updates | updates | 1 | 1 |
| 33 | 1235635793793867777 | updates | updates | 1 | 1 |
| 34 | 1235635720578007041 | updates | updates | 1 | 1 |
| 35 | 1235598769543991302 | flood alert tidal river ouse from naburn lock ... | flood alert lock march | 11 | 4 |
| 36 | 1235596055607160834 | back to normal river ouse at naburn lock march | back normal lock march | 9 | 4 |
| 37 | 1235596032861507586 | back to normal upper river ouse march | back normal upper march | 7 | 4 |
| 38 | 1235586941569163264 | from the majority of mps vo | 6 | 0 | |
| 39 | 1235584978978775044 | julie foley on the flood coastal erosion risk ... | on flood erosion risk creating places g | 16 | 7 |
| 40 | 1235583883220934659 | rt hi can you confirm if was able to access th... | can confirm was able access scheme pay extende... | 21 | 10 |
| 41 | 1235578669684252678 | rt council flood risk manager and pm steve wra... | flood risk pm future flood protection discuss | 16 | 7 |
| 42 | 1235578487798300677 | thanks for sharing information about our surve... | sharing information about survey | 12 | 4 |
| 43 | 1235577853632266242 | council flood risk manager and pm steve wragg ... | flood risk pm future flood protection d | 15 | 7 |
| 44 | 1235567773956349952 | boris johnson defends decision not to visit uk... | defends decision visit s flood hit areas servi... | 21 | 11 |
| 45 | 1235567346342715394 | feelings flood your interactions with others t... | feelings flood today a great more sagittarius | 13 | 7 |
| 46 | 1235565759637721095 | rt today i m at the conference to learn and sh... | today i m learn share experience working toget... | 20 | 11 |
| 48 | 1235533364846833666 | route flood update east west cowick is now ope... | route flood update east west is now open bus w... | 20 | 13 |
| 49 | 1235526957179133953 | news litres of water is being pumped out of lo... | news water is being pumped out lower second sa... | 17 | 10 |
| 51 | 1235518102105919495 | hi can you confirm if was able to access the b... | can confirm was able access scheme pay extende... | 20 | 10 |
| 52 | 1235512928704647168 | some key messages from the minister of floodin... | some key messages minister flooding capital in... | 21 | 8 |
| 53 | 1235505901613826048 | today i m at the conference to learn and share... | today i m learn share experience working toget... | 18 | 11 |
| 57 | 1235495211494764547 | it s great to see new walk being hosed down so... | s great see new walk being down so flood water... | 22 | 13 |
| 58 | 1235493008512073728 | okay flood i was already familiar with and i r... | okay flood i was familiar i realized weather o... | 18 | 10 |
| 59 | 1235482864030027778 | flood warning river ouse at naburn lock march | flood warning lock march | 8 | 4 |
| 60 | 1235482841267556353 | flood alert upper river ouse march | flood alert upper march | 6 | 4 |
| 61 | 1235482818555215873 | flood alert tidal river ouse from naburn lock ... | flood alert lock march | 11 | 4 |
| 62 | 1235358937937063939 | rt protect cities from flooding by making them... | flooding making wilder better people make spac... | 22 | 9 |
| 66 | 1235316708715397120 | flood warning river ouse at naburn lock march | flood warning lock march | 8 | 4 |
| 67 | 1235316685831315464 | flood alert upper river ouse march | flood alert upper march | 6 | 4 |
| 68 | 1235311644194074626 | flood alert tidal river ouse from naburn lock ... | flood alert lock march | 11 | 4 |
| 73 | 1235291158923956227 | the flood barriers aren t working so the govt ... | flood t working so have told try blocking roll | 19 | 9 |
| 75 | 1235252399121539073 | rt river ouse york water level m at chance of ... | water level m chance flooding flood falling | 15 | 7 |
| 77 | 1235248670355972102 | river ouse york water level m at chance of flo... | water level m chance flooding flood falling | 14 | 7 |
| 78 | 1235237319558475777 | rt river ouse york water level m at chance of ... | water level m chance flooding flood falling | 15 | 7 |
| 79 | 1235233686649528320 | flood alert upper river ouse march | flood alert upper march | 6 | 4 |
Step 2: Wordcloud generation¶
In [23]:
#generate new wordcloud
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
text = clean_df.English_text
wordcloud = WordCloud(
width = 3000,
height = 2000,
background_color = 'black',
stopwords = STOPWORDS).generate(str(text))
fig = plt.figure(
figsize = (40, 30),
facecolor = 'k',
edgecolor = 'k')
plt.imshow(wordcloud, interpolation = 'bilinear')
plt.axis('off')
plt.tight_layout(pad=0)
plt.show()
#plt.savefig("wordcloud.png")
Step 3: most used words in the twitter data¶¶
In [18]:
## Inspecting the most common words used in all the observed english tweets
# Breaking into individual words every tweet
def my_tokenizer(text):
return text.split() if text != None else []
# Breaking all english tweets into individual words and returning a list of these words
tokens = english_df.English_text.map(my_tokenizer).sum()
# most common words in all tweets (stop words included)
counter_wsw = Counter(tokens)
counter_wsw.most_common(20)
# creating a data frame for the most common words with stop words in the tweets for plotting
# >2 to exclude single and doulble lettered words that aren't included among stop words
word_freq_wsw = pd.DataFrame.from_records([x for x in counter_wsw.most_common(30) if len(x[0])> 2], columns = ['tokens', 'count'])
# removal of stop words and rechecking the most frequent words
def remove_stopwords(tokens):
return [t for t in tokens if t not in STOP_WORDS]
counter = Counter(remove_stopwords(tokens))
# creating a data frame for the most common words without stop words in the tweets for plotting
word_freq = pd.DataFrame.from_records([x for x in counter.most_common(30) if len(x[0])> 2], columns = ['tokens', 'count'])
# frequency distribution of the top 19 most used words in the twitter data
word_freq.plot(kind = 'bar', x = 'tokens')
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x10f300f50>
In [19]:
# tokens without stop words and single and double lettered words
tokens_nsw = [x for x in remove_stopwords(tokens) if len(x) > 2]
bigrams = nltk.bigrams(tokens_nsw)
bi_count = Counter(bigrams)
#creating a data frame for the most common words without stop words in the tweets for plotting
bigram_freq = pd.DataFrame.from_records(bi_count.most_common(20), columns = ['tokens', 'count'])
bigram_freq.plot(kind = 'bar', x = 'tokens')
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2f8bda90>
3. Visualizaton of geo-tagged tweets¶
Geo-location is another important feature of social media. Location of social media can be used in understanding urban mobilities, xxx and xxx. In emergency management, social media data can be used as crowdsourcing tool to collect real-time information. In this section, we will subset tweets with geotagged location to identified the effected areas may suffer flood or storms in the early spring 2020. Because of the limted time of supervision, we will use pre-collected data (data collected in a week) to demonstrate how to process and visualize geo-location of tweets.
Data
- wards
- population
- flood
Process
- ward and population join
- summarise
- calculation
- visualization
- It is suggested to create a folder and name it as
rm03_YourCRSid_sup2, at your prefered directory on your disk. This folder will be the working directory for the assignment and supervision. - Download dataset (sample data of Sejong, South Korea): Urban_2018.zip, Exclusion_2014.zip, Road_2018.zip, and Boundary.zip in the working directory (you don’t need to unzip).
QGIS Project Setup (5 mins)¶
- In the menu bar, Click
Project>Newto create a new QGIS project. - Go to
Project>Save Asand save assupervision1.QGZto the working directory. - Go to
Project>Propertiesand open theProject Propertieswindow.Generaltab: in the general settings, set your working directory asProject Home, change the unit for distance measurement you prefer and also display coordinates units.Metadatatab: It is suggested to input title, author, creation date and a short abstract in the identification tab.CRStab: this tab provides Coordinate Reference System (CRS) setting for the project file. Here, we choose the projected coordinate system,OSGB 1936/British National Grid EPSG:27700. Be aware that CRS setting in theProject Propertiesis just for the project (calledData Frame settingin ArcGIS). CRS setting for layers will be introduced later.
Note: after addingProject home, you can findProject Homedirectory is showing in theBrowser panel. It is much easier to locate your data files through this panel.
Making heatmap based on their location (5Mins)¶
- How to import data from spreadsheets and CSV with coordinates?
- How to display coordinates from spreadsheets and CSV in QGIS?
Importing spreadsheets or CSV files
- Download
York_floods_tweetsdata of Cambridgeshire from: Cambridgeshire Insight Open Data{:target="_blank"} and save into your working directory. This is a set of data to be used to geo-locate a shortlist of agencies and facilities around Cambridge. - Navigate to menu bar click
Layer>Add Layer>Add Delimited Text Layer. Browse theYork_floods_tweets.csvjust downloaded and change the layer name toYork_floods_tweets. In the section of File Format, choose CSV. In the Geometry Definition section, choosePoint coordinatesand selectLongitudeandLatitudefields as X Y fields respectively. Normally the Geometry definition section will be auto-populated if it finds a suitable X and Y coordinate fields. Then choose the right CRS (EPSG:4326 - WGS84) for this file. Finally, click add and you will find a point layer.
Making heatmap for geotagged tweets
Find
QuickMapServicesfunction in theWebsection from the menu bar. ChooseOSM-OSM Standard, and you will add base map in QGIS.Double click the
York_floods_tweetslayer to get in properties dialog.- In the Properties dialog, switch to the Style tab. Select
Heatmapas the renderer. You have a lot of choice of color-ramps for the heatmap. - Set
10 millermeterfor radius and click OK.
Using print layout¶
The Layout Manager
QGIS allows you to create multiple maps using the same map file. For this reason, it has a tool called the Layout Manager.
- Click on the
Project->Layout Managermenu entry to open this tool. You’ll see a blank Layout manager dialog appear. - Click the Add button and give the new layout the name of
York_floods_tweets_heatmap. - Click OK.
- Click the Show button.
Basic Map Composition
In this example, the composition was already the way we wanted it. Ensure that yours is as well.
- In the Print Layout window, check that the values under Composition ‣ Paper and Quality are set to the following:
Size: A4 (210x297mm)
Orientation: Landscape
Quality: 300dpiNow you’ve got the page layout the way you wanted it, but this page is still blank. To add the map, click on the
Add New Mapbutton:
. With this tool activated, you’ll be able to place a map on the page.Click and drag a box on the blank page:
. The map will appear on the page.Move the map by clicking and dragging it around:
Note:
- Zoom in and out on the page (but not the map!) by using these buttons
. - Zoom and pan the map in the main QGIS window. You can also pan the map using the Move item content tool:
move Item Content.
Because a Layout in QGIS is part of the main map file, you’ll need to save your main project. Go to the main QGIS window (the one with the Layers panel and all the other familiar elements you were working with before), and save your project from there as usual.
Adding Map Elements
Now your map is looking good on the page, but your readers/users are not being told what’s going on yet. They need some context, which is what you’ll provide for them by adding map elements. First, let’s add a title.
Add Title
- Click on this button: label
- Click on the page, above the map, and a label will appear at the top of the map. Resize it and place it in the top center of the page. It can be resized and moved in the same way that you resized and moved the map.
- As you move the title, you’ll notice that guidelines appear to help you position the title in the center of the page.
Add North Arrow
- Click on this button: label
- Click on the page, above the map, and a label will appear at the top of the map. Resize it and place it in the top center of the page. It can be resized and moved in the same way that you resized and moved the map.
- As you move the title, you’ll notice that guidelines appear to help you position the title in the center of the page.
Add Scale
- Click on this button: label
- Click on the page, above the map, and a label will appear at the top of the map. Resize it and place it in the top center of the page. It can be resized and moved in the same way that you resized and moved the map.
- As you move the title, you’ll notice that guidelines appear to help you position the title in the center of the page.
Add Legend
- Click on this button: addLegend
- Click on the page to place the legend, and move it to where you want it:
Not everything on the legend is necessary, so let’s remove some unwanted items.
In the Item Properties tab, you’ll find the Legend items panel.
- Select the buildings entry.
- Delete it from the legend by clicking the minus button: signMinus
You can also rename items.Select a layer from the same list.
- Click the Edit button: edit
- Rename the layers to Places, Roads and Streets, Surafce Water, and Rivers.
Set landuse to Hidden, then click the down arrow and edit each category to name them on the legend. You can also reorder the items:
Adding elements is the basic step for a map. To make a good map, you need to do more! Check the experts' guidence from ESRI illustrating how to make a meaning map by answeing 10 questions.
.
Exporting Your Map
Finally the map is ready for export!
- Find export buttons near the top left corner of the Layout window:
- Choose
Export as Image
In [ ]: